Predicting the risk of acute kidney injury after hematopoietic stem cell transplantation: development of a new predictive nomogram

The purpose was to predict the risk of acute kidney injury (AKI) within 100 days after hematopoietic stem cell transplantation (HSCT) in patients with hematologic disease by using a new predictive nomogram. Collect clinical data of patients with hematologic disease undergoing HSCT in our hospital from August 2012 to March 2018. Parameters with non-zero coefficients were selected by the Least Absolute Selection Operator (LASSO). Then these parameters were selected to build a new predictive nomogram model. Receiver operating characteristic (ROC) curve, calibration curve, C-index, and decision curve analysis (DCA) were used for the validation of the evaluation model. Finally, the nomogram was further evaluated by internal verification. According to 2012 Kidney Disease Improving Global Guidelines (KDIGO) diagnostic criteria, among 144 patients, the occurrence of AKI within 100 days after HSCT The rate was 29.2% (42/144). The C-index of the nomogram was 0.842. The C-value calculated by the internal verification was 0.809. The AUC was 0.842, and The DCA range of the predicted nomogram was from 0.01 to 0.71. This article established a high-precision nomogram for the first time for predicting the risk of AKI within 100 days after HSCT in patients with hematologic diseases. The nomogram had good clinical validity and reliability. For clinicians, it was very important to prevent AKI after HSCT.

Data collection. This study collected clinical data of hematologic diseases patients who underwent HSCT, including gender, age, weight, basic creatinine level, stem cell source, donor source, human leukocyte antigen (HLA) and blood type matching, total body irradiation (TBI), cyclosporin A(CSA), Tacrolimus (FK506), vancomycin, amphotericin B, triazole antifungals, ganciclovir, aGVHD, HC, engraftment syndrome, secondary hypertension, secondary diabetes, infection. The patients were divided into two groups, the AKI group, and the non-AKI group. The criteria for infection were that the patient must had an axillary temperature above 37.7 °C for 1 h or an axillary temperature above 38.0 °C as previously reported 12 . In addition, we also referred to previous literature reports for the standard of aGVHD grade (Table 1) 13,14 . In this study, no patients were diagnosed with TMA. We used peripheral or bone marrow hematopoietic stem cells for patient transplantation. R software (Vienna, Austria, https:// www.R-proje ct. org) was used for data analysis. The LASSO method was used for all clinical data analysis. Parameters with non-zero coefficients were selected to establish a predictive model. The nomogram was based on a score for each parameter, and the scores for each parameter were added together to obtain a total score. Each total score corresponded to the probability of an outcome event occurring. The discriminative ability and prediction accuracy of the nomogram were evaluated by C-index. The range of the C-index was usually < 0.5, 0.5-0.7, 0.7-0.9, and > 0.9, which represent low accuracy, medium accuracy, high accuracy, and extreme accuracy, respectively 15 . The calibration curve was used to evaluate the actual risk and predicted risk of the AKI nomogram. The predictive ability of the nomogram was evaluated by the AUC curve. The clinical net benefit was evaluated the DCA curve. Finally, we chose an internal verification method to verify the nomogram. The C-index was calculated through the bootstrapping verification of the AKI nomogram (1000 bootstraps resamples).
Ethics approval and consent to participate. All procedures were performed following relevant guidelines. This paper has been approved by the ethics committee of The First Affiliated Hospital of Guangxi Medical University. Written informed consent of patients has been obtained for this study.

Results
Patients' characteristics. A total of 144 patients with hematological diseases who underwent HSCT were collected from August 2012 to March 2018 in our hospital, including 66 males and 78 females. Among all patients, 42 cases developed AKI and 102 cases without AKI. The data of the two groups were shown in Table 2, including the general data of the patients, transplant characteristics, drugs used, postoperative complications, and other information. In this study, a total of 42 patients with AKI were included for analysis, including 11 with stage I, 16 with stage II, and 15 with stage III renal impairment.
All data were analyzed by the LASSO regression analysis. A total of 7 parameters with non-zero coefficients were obtained. The binomial deviation ( Fig. 1) and coefficient ( Fig. 2) were obtained under the optimal lambda by LASSO analysis. A new nomogram was constructed to predict the risk of AKI within 100 days after HSCT (Fig. 3). The red dot for each parameter was specific information for that patient. The total score obtained for this patient was 227 points, and the predicted probability of AKI was 88.3% (Fig. 3).
The C-index was measured to evaluate the predictive ability of the new nomogram, and the C-index was 0.842. The calibration curve was close to the ideal curve, indicating that the model had a good predictive ability (Fig. 4). www.nature.com/scientificreports/ The ROC curve was further constructed, and the AUC was calculated to be 0.842 (Fig. 5). The net benefit of the predictive nomogram ranged from 0.01 to 0.71, which was determined by DCA curve (Fig. 6).
To further verify the actual predictive ability and stability of the nomogram. we chose an internal verification method. The C-index was analyzed by the bootstrap verification of the nomogram (1,000 bootstrap resampling). The C-index was 0.809, which was a very close C-index of 0.842 for the training set.

Discussion
AKI is a common complication after HSCT. The increased severity of AKI was associated with an increased risk of death 7 . The pathogenesis of AKI after HSCT was complicated and affected by many factors. Prerenal azotemia was a common cause of AKI in HSCT patients. Common adverse reactions to chemotherapy include nausea, vomiting, diarrhea, and mucositis, which often caused excessive fluid loss through the gastrointestinal tract or insufficient oral intake to cause circulation blood volume to be reduced, which eventually resulted in prerenal kidney injury 16,17 . In addition, acute tubular necrosis was also a common cause of AKI after HSCT. Hypovolemic shock, septic shock, or nephrotoxic drugs, such as amphotericin B, vancomycin and CSA could cause acute renal tubular necrosis and cause renal AKI, which could cause AKI alone or in synergy with prerenal etiology [16][17][18] . Urinary obstruction might be the cause of AKI in patients with HSCT. Intravenous infusion of ganciclovir and Table 2. Comparison of clinical data between AKI group and non-AKI group. Tacrolimus (FK506), acute graft versus host disease (aGVHD), hemorrhagic cystitis (HC), total body irradiation (TBI), cyclosporin A(CSA). *P < 0.05, **P < 0.01.  Figure 1. The LASSO model was constructed to select the optimal parameters (lambda) and the relationship graph between binomial deviance and log (lambda) was drawn. www.nature.com/scientificreports/ other antiviral drugs could precipitate in the urine and form crystals in the renal tubules, causing obstruction, and blood clots formed by HC could lead to urinary tract obstruction, resulting in obstructive postrenal AKI 19 . Although these complications might not be independent risk factors for AKI, their combination might lead to the occurrence of AKI. In addition, nephrotoxic drugs used to treat these complications could also cause AKI 20 .In addition, hypertension and diabetes also lead to the occurrence of AKI after HSCT 3,18 . Several risk factors for AKI in patients undergoing HSCT had been reported. The descriptions of transplant characteristics, such as donor, race, TBI, nephrotoxic agents, and post-transplant adverse events, such as aGVHD and infection, were inconsistently described as risk factors for the development of AKI [21][22][23] . Studies had shown that unrelated donors were closely associated with AKI (HR, 6.26; P = 0.042) 20 .It had been reported that transplantation of hematopoietic  www.nature.com/scientificreports/ stem cells from unrelated donors was associated with a significant increase in the risk of infection, severe aGVHD, and organ toxicity [24][25][26][27][28][29] . Calcineurin inhibitor (CNIs) caused AKI by arteriolar vasoconstriction, reducing kidney perfusion, tubular toxicity, and endothelial injury 30,31 . CNIs nephrotoxicity after hematopoietic transplantation was reported in up to 31% of patients 32 . The infection could lead to hemodynamic changes and inflammatory damage, leading to AKI. The infection resulted in systemic arteriole constriction and endothelial damage, causing capillary leakage and renal insufficiency 33 . Damage to the tubules themselves led to the release of local cytokines and chemokines, resulting in local inflammation and further intrarerenal damage 34 . In addition, antimicrobials commonly used to treat infection were often nephrotoxic. However, the mechanism of AKI induced by triazole antifungals remained unclear 35 . Liu et al. found that HLA mismatched was closely related to AKI after HSCT (OR = 3.6; 95%CI = 1.1-13.0) 36 . And ABO mismatched was found to be associated with a significantly increased risk of grade II-IV aGVHD 37 . This might be the reason why ABO mismatched were associated with AKI. AKI after HSCT could seriously affect the survival and prognosis of patients, so it was important to identify the risk of AKI in advance. At present, studies were using the hematopoietic cell transplantation-specific comorbidity index (HCT-CI) to study the incidence of AKI after allogeneic HSCT. HCT-CI factors included high disease risk, related donor, myeloablative conditioning regimen, stem cell source, prior stem cell transplant 38 . At present, this study was the first time to use the nomogram model to predict the risk of AKI within 100 days after HSCT.  www.nature.com/scientificreports/ In this paper, we introduced perioperative parameters to develop the nomogram of predicting AKI risk. The C-index was 0.842 and 0.809 in the training and validation set respectively, which revealed that the prediction ability of the nomogram was characterized by high accuracy 15 . The study showed that the higher the C-index of internal validation, the better the efficiency of identification and comparison 15 . In addition, we performed the decision curve analysis to estimate the actual clinical benefit of the nomogram. The results of DCA also showed high validity and predictive effect. The risk of AKI after HSCT could be predicted in advance by using the nomograph model, which could guide the management of HSCT patients, improve the survival of patients and improve the quality of life of patients. The results of this study showed that the factors included in predicting the risk of AKI after HSCT were donor, HLA, blood type matching, FK506, aGVHD, infection and triazole antifungal drugs.

Non-AKI group (N = 102) AKI group (N = 42) P-value
However, this paper had some limitations. (1) This study was a retrospective analysis without a prospective study. (2) More cases need to be added to verify the nomogram.

Conclusion
In the article, the nomogram had good clinical validity and reliability. We found some factors that can be used to predict the risk of AKI within 100 days after HSCT, including donor source, HLA, blood type matching, FK506, aGVHD, infection, and triazole antifungal drugs. The nomogram can be used to predict the risk of AKI to optimize patient management during the diagnosis and treatment of HSCT.

Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.